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Robust estimation in partially linear regression models with monotonicity constraints
(Taylor & Francis, 2019-11)
Partially linear models are important tools in statistical modelling, combining the flexibility of non–parametric models and the simple interpretation of linear models. Monotonicity constraints appear naturally in certain ...
On a robust local estimator for the scale function in heteroscedastic nonparametric regression
(Elsevier Science, 2010-08)
When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function ...
Robust and sparse estimators for linear regression models
(Elsevier Science, 2017-07)
Penalized regression estimators are popular tools for the analysis of sparse and high-dimensional models. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the ...
A multivariate ultrastructural errors-in-variables model with equation error
(ELSEVIER INC, 2011)
This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented ...
Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression ModelsCorrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models
(Sociedade Brasileira de Econometria, 2008)
Continuity and differentiability of regression M functionals
(Institute of Mathematical Statistics, 2012-11)
This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S ...
Robust regression based on shrinkage with application to Living Environment Deprivation
(Springer, 2020-01-01)
A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously studied for outlier detection in multivariate ...
Reflection on logistic regression and clinical decisions [Reflexión acerca de la regresión logística y las decisiones clínicas]
(Circulo Medico de Rosario, 2018)